Inductive Learning by Examples is the process by which a model of a given phenomenon is constructed by repeated exposure to representative examples. This is a supervised learning technique; i.e., the learner is given the desired output in addition to a feature-based description of the example. The goal is to minimize the error between this desired output and the predicted output of the model, both for the examples presented to the model, as well as for future examples not yet seen. Techniques for Inductive Learning by Examples include, without limitation, regression, Bayesian models, neural networks, decision trees, support vector machines, and variants thereon.
The inductive learning paradigm has been broadly adopted within a number of verticals including retail, health and pharma, manufacturing, financial, etc. with great success in recent years—a key aspect of this success is that in many cases, models constructed in this manner will outperform their human counterparts with respect to predictive accuracy. The reason for this superior performance lies in the tradeoff between the complexity of the reasoning process and the complexity of the data. While the human intellect is still far superior in its breadth and depth of reasoning capabilities, short-term memory and other capacity limitation make it a poor choice for the inductive processing of large data sets: in this case, a simpler algorithm repeatedly applied to this set will often yield superior results.
However, the fact that the models themselves are constructed purely algorithmically does not imply that the entire process is automatic; in fact, a number of choices must be made by data scientists or other similar data experts before and during this process if high quality results are to be obtained.
In particular, two key areas involving considerable human intervention, hindering complete automation and inhibiting the formation of optimal results, remain. The first such area is the selection of models from an ensemble of models on the basis of the current example during scoring (i.e., making a prediction for that example). The second area falls under broad umbrella of inductive bias, that is, the set of collective decisions before an algorithm is applied to a data set that skews the predictive results in one direction or another.